Microseisms are induced in the reservoir rock matrix due to pore pressure perturbation and geomechanical stress field relaxation as the reservoir fluids are produced and injected. The micro-earthquakes are generated because the stress field in the reservoir is anisotropic. As the in situ stresses are perturbed by reservoir production and injection activities, the resulting changes in fluid pressure create elastic failure in the rocks and cause microseismic events that are detected with special seismic sensors.
Microseisms, emanated from the reservoir, with local magnitude (ML) down to a Richter value <−1 or even lower, are detected. Events below magnitude −3 are often classified as background noise. These microseisms are detected in multi-component seismic sensors with wide bandwith, over distances of 1 km. and more. Conventionally the assessment of changes in the reservoir characteristics over the production time or reservoir fluid flow monitering is achieved with measurements that were in selected wells with down hole instruments only at selected production time intervals.
The Richter local magnitude ML and seismic moment M0 are computed using the formula:
                              M          L                =                                            Log              10                        ⁡                          (                                                M                  0                                -                16                            )                                1.5                                    (        1        )            where the seismic moment M0 is computed using the formula from Lee, W. H. K. & Stewart, S. W., Advances in Geophysics, Supp. 2, Principles and Applications of Microearthquake Networks, (Academic Press, 1981):
                              M          0                =                              4            ⁢                                                  ⁢            π            ⁢                                                  ⁢            ρ            ⁢                                                  ⁢                          V              S              3                        ⁢                          W              0                        ⁢            R                    .85                                    (        2        )            R is the source-receiver distance, W0 is a vorticity parameter, 0.85 is the assumed radiation coefficient, ρ is the bulk density of rocks, and Vs is the shear wave velocity.
The present invention offers a complementary and, in the presence of suitable reservoir rock and fluid properties, an alternative tool for monitoring hydrocarbon reservoirs with time-lapse seismic using-permanent sensors, disclosed in U.S. Pat. No. 5,946,271 to Dragoset. Such inter-well monitoring is also known as four dimensional or 4D seismic that is applied for monitoring the water sweep in a reservoir, disclosed in U.S. Pat. No. 6,886,632 to Raghuraman et al.
As water replaces the hydrocarbon fluids in some reservoirs, the resulting saturation change, generates only very small alterations in compressibility and hence in the seismic acoustic properties. This is especially true in hydrocarbon reservoirs with stiff carbonate rock matrix and low compressibility fluids like oil and water. Most of the giant oil fields in the Middle East with carbonate reservoirs have such characteristics. As a result, conventional technique like 4D seismic has only marginal utility in monitoring water sweep in these reservoir settings.
Results from recent modeling studies of Arab-D reservoir in the super-giant Ghawar oil field of Saudi Arabia suggest that only small changes in the acoustic properties occur with changes in pore fluid saturation. The reservoir pore saturation changes in the reservoir are due the injected water replacing the extracted oil. The sensitivity of the resulting change in seismic signature in carbonate reservoirs like Arab-D, is extremely low and is often below the detectability of 4D seismic measurements, as described in Dasgupta, S., “When 4D Seismic is Not Applicable: Alternative Monitoring Scenarios for the Arab-D Reservoir in the Ghawar Field”, Geophysical Prospecting, Vol. 53, pp. 215-227, 2005.
The application of microseismic technique, however, would be unlikely to detect reservoir pore saturation changes as oil is swept by injected water. It would instead shed light on the fluid pathways. Information on fluid encroachment paths would allow for detection of premature water breakthroughs in production wells; i.e., an early warning system.
Accurate monitoring of fluid pathways and delineating the reservoir fluid flow anisotropy optimizes the reservoir management and improves the recovery of oil from these reservoirs. These advantages could be achieved by application of microseismic emissions for detecting anomalous sweep behavior. Such monitoring would also provide an opportunity for remedial design and for optimizing the planning of production and injection well locations for field development. Accurate monitoring also increases the accuracy of reservoir simulation models.
Uniform hydrocarbon reservoir fluid fronts and drainage are rare in active fields in production operations. Reservoir characteristics and drainage patterns in most fields are often proven to be much more complex than is initially assumed and are further complicated as the production field matures. The fluid flow anisotropy is related to the heterogeneity in reservoir rocks. The existence of joints, bedding planes, faults, and fractures are common in the sedimentary rock matrix. In most reservoirs, the in-situ stress conditions due to overburden pressure keep these features closed to fluid flow. During the producing life of a hydrocarbon reservoir, physical changes such as fluid pressures result in perturbation in the in-situ stresses.
The reservoir stress-field is designed by a conjugate set of axes defined as principal stresses, with σ1 being the maximum stress, σ3 being the minimum stress, and σ2 being the intermediate stress. The stress axes are mutually orthogonal to each other. In order for rock deformation to take place, the principal stress in one direction σ1 must exceed the other two principal stresses σ2 and σ3. Along the zones of weakness in reservoir rocks, failure occurs that is defined by the known Mohr-Coulomb failure criterion. The increase in differential stress between the maximum and minimum stresses, or a net decrease in the effective normal stress due to the difference between normal stress and pore pressure, causes slippages or rock failure, as described in Marsden, J. E. & Hughes, T. J. R., Mathematical Foundations of Elasticity, Dover Publications, New York, 1994.
The following equation defines the Mohr-Coulomb rock failure:τf=τ0+σtan φ  (3)where τ0=equilibrium stress state, τf=shear stress at failure, σ=applied resultant stress, and φ=angle of internal friction.
Such failure or shear-slippage induces microseismic activities and is caused by stress changes in reservoirs with perturbation caused by injection and production. Rock failure can be graphically visualized to occur as the differential stress is increased and the Mohr circle intersects the failure envelope. This occurs due to increase in maximum principal stress or a decrease in minimum principal stress.
Reservoir pore pressure change is a major factor in in-situ stress alteration resulting in Mohr-Coulomb failure. Effective stress alteration occurs due to pore pressure changes. Increase in pore pressure due to water injection reduce the effective strength of fractures, joints and faults below the critical shear stress, causing rock failure and thus trigger microseisms. Similarly, oil production from permeable rocks reduces the pore pressure relative to the surrounding lower permeable rocks. This causes a pore pressure gradient and local stress concentrations. Poroelastic changes due to oil production from an Arab-D reservoir concentrates the shear stresses near the reservoir edges or the water flood front. Microseismic events are expected to be concentrated above and below the reservoir.
As the reservoir stress is perturbed by fluid injection, shear slippage or rock failure occurs along the zones of weakness like fractures and faults. The shear slippage in rocks generates microseismic activity. These microseisms are detected and their source or hypocenters are located using broad bandwidth borehole sensors. For each microseismic event, it is first necessary to determine the fault plane and slip direction (i.e., the source mechanism) before investigating the source parameters.
This analysis is more difficult when only one observation well is available. If several sensors are emplaced in multiple wells and sensors that are widely distributed in space are available, generalized triangulation techniques can resolve the microseismic source locations with high accuracy. The distribution of the sensors relative to the microseismic event source location in the reservoir volume determines the efficiency of the sensor network. Optimum network design of sensor locations is derived by forward modeling and using elastic wave velocities and geomechanical properties of rock formations in the study area. The first arrival times of recorded compressional waves (P-waves) and shear waves (S-waves) and the velocities of the rock layers are used to compute source. location or hypocenter microseisms where the rock failure occurred.
Drilling multiple wells for microseismic, however, can prove uneconomical, especially for deeper reservoirs, for which the cost of drilling multiple observation wells becomes prohibitive. Instead of drilling several observation wells for detecting microseisms, in the current invention, the network design consists of a large number of multi-component sensors spatially distributed on the ground surface and cemented permanently in the vicinity of a well. In addition, multi-component sensors are cemented or clamped inside the well bore or borehole at multiple levels in a single well. Such a network provides the capability of detecting a large number of microseismic signals over a wide 3D aperture. The increased density of distributed measurements with respect to the microseismic events in the reservoir ensures that their source points or hypocenters are located accurately.
The method for locating the microseismic source has been disclosed in U.S. Pat. No. 6,049,508 to Deflandre, and U.S. Pat. No. 6,920,083 to Therond et al. Such source location techniques are implemented by identifying and classifying the first arrival time breaks and measuring arrival times of P-wave (or compressional wave) and S-wave (or shear wave).
Recorded microseismic waves consist of records of P-wave and S-wave. The amplitudes of these P-waves and S-waves are detected and the seismograms are recorded.
Also, polarization analysis is performed with hodograms or terminus of a moving vector for particle motion of the waves recorded in the three component (3C) sensors which are oriented orthogonally in the sensor package. The polarization analysis consists in measuring the spatial distribution of a 3C (right-normal basis) signal over a time window using the covariance matrix. Most of the time, the results used are the “azimuth” and the dip inclination of the distribution main direction which is defined by a vector. This analysis determines the direction of a wave's approach to the 3C sensors or detectors that are planted precisely with a known orientation. With these information and the seismic wave propagation velocities in the reservoir and overburden rocks, the distances between the sensors and the microseismic source in the reservoir are computed.
The particle motion of the P-wave defines the direction of the microseismic source from the observation point at each sensor. The plurality of three component sensors in the borehole and spatially distributed over a network on the ground surface provides a redundancy of observations for the same microseismic source. Such a network provides a mechanism for accurate determination of their location. Microseismic events can be located in space and their distribution patterns interpreted in terms of fluid conduction paths, sealing faults or homogeneous sweep. This information will provide improved reservoir management and will allow better planning for future wells.
The analysis of recorded P-wave and S-wave amplitude data from the three component sensors provides orientation and direction of the shear slippage in the reservoir as production and injection activities continues. The ratio of the measured S-wave amplitude and P-wave amplitude (S/P ratio) is computed at the microseismic source location. The detected S/P amplitude ratios are compared with predicted values based on geomechanical failure model and their spatial distribution matched using forward modeling. The data determine the rock failure mechanism and their orientation. Reservoir fluids advance preferentially in directions defined by the orientation and distribution of these failure surfaces. Consequently, the failure surface defines the pathways for preferential fluid movement in the reservoir.
From the Lee, W. H. K. & Stewart, S. W., Advances in Geophysics, Supp. 2, Principles and Applications of Microearthquake Networks, (Academic Press, 1981); and also Raymer, D. et al., “Genetic Algorithm Design of Microseismic Injection-Monitoring Networks in the Tengiz Field”, SEG Technical Program Expanded Abstracts, 2000, pp. 562-565; the travel time for induced microseismic events from source to receiver involves solving a set of first-order differential equations. A network of sensors distributed spatially on the surface and at different levels in a borehole records a number of arrival times n, for P-waves and S-waves from a microseismic event with hypocenter parameters (x, y, z, t). In matrix notation, the problem of solving the following set of linear equations of condition:AX=B  (4)where A is the n×4 design matrix of partial differentials, X is a vector of four unknown hypocenter parameters (x, y, z, t) and B represents vector differences between the calculated and observed travel times arrival. The design matrix determines the efficiency of the network. For a given matrix A and a set of observations of B, the equation will solve for unknown vector X. The partial differentials define how much the hypocenter parameters will change with respect to travel times. The uncertainty will be large when small changes in travel time cause large changes in hypocenter. This provides a quantitative measure for network performance in locating a microseismic event source.
Performance of the network is evaluated by populating the reservoir volume of interest with trial locations. On this volume, 3D seismic ray trace modeling is performed between the trial locations and the designated sensor positions to produce a complete set of partial differentials. Each partial differential forms a line of the design matrix. The optimal combination of sensor locations in the network is found by solving these equations.